In-Depth: Deploy with Replicate Cog

In this tutorial, we will deploy an endpoint built with Cog framework by Replicate, for packaging and running machine learning models.

We will deploy the black-forest-labs/FLUX.1-schnell image generation model.

black-forest-labs/FLUX.1-schnell is a 12 billion parameter rectified flow transformer by Black Forest Labs, capable of generating images from text descriptions.

Prerequisites

For this example you need a Python environment running on your local machine, a Docker (or Docker-compatible) container runtime installed on your computer. A container registry to store the image created by Cog and DataCrunch cloud account to create a deployment.

Docker container runtime

Docker is a platform for developing, shipping, and running applications. You can learn how to set up Docker from the official Docker website. Note that you can use any Docker-compatible container runtime, such as:

Python environment

We are using Python version 3.12 for this tutorial. You can set up your Python environment as you see fit, however we are using venv combined with bash shell for this example.

Cog

You will need to have Cog installed on your computer. Please follow the installation instructions and choose your preferred method of setting up Cog.

Container Registry

You will need a container registry to store the container image. You can use any container registry you prefer. In this example we use GitHub Container Registry. You can find more information about GitHub Container Registry from the official GitHub documentation.

For the sake of our example, we will use nonexistent GitHub registry url ghcr.io/username/container-image In the examples remember to replace this with your own GitHub registry url.

Please make sure that you have credentials to login to your registry. You can login to GitHub container registry by typing the following command:

docker login <registry-url> -u <registry-username>

Create a container image

Next we will create a container image. Please create a folder named flux-schnell and save the following files in it, starting with cog.yaml, defining the dependencies and the predictor class required to run the model:

build:
  gpu: true
  python_version: "3.12"
  python_packages:
    - diffusers
    - transformers
    - accelerate
    - torch
    - cog
    - sentencepiece
    - protobuf
    - hf_transfer
predict: "predict.py:Predictor"

Next, please create predict.py, containing the Predictor class needed for setting up and running the model:

from typing import Any
from cog import BasePredictor, Input
from diffusers import FluxPipeline
from io import BytesIO
import torch
import base64

class Predictor(BasePredictor):
    def __init__(self):
        self.pipe = None

    def setup(self) -> None:
        self.pipe = FluxPipeline.from_pretrained(
            "black-forest-labs/FLUX.1-schnell",
            torch_dtype=torch.float16,
            use_safetensors=True
        )
        self.pipe.to("cuda")

    def predict(
        self,
            prompt: str = Input(
                description="The text prompt to generate the image.",
                default="A photo of a cat"
            ),
            guidance_scale: float = Input(
                description="Guidance scale parameter.",
                default=0.0
            ),
            height: int = Input(
                description="Height of the generated image.",
                default=1024
            ),
            width: int = Input(
                description="Width of the generated image.",
                default=1024
            ),
            num_inference_steps: int = Input(
                description="Number of inference steps.",
                default=4
            ),
            max_sequence_length: int = Input(
                description="Maximum sequence length.",
                default=256
            )
    ) -> Any:
        images = self.pipe(
            prompt=prompt,
            guidance_scale=guidance_scale,
            height=height,
            width=width,
            num_inference_steps=num_inference_steps,
            max_sequence_length=max_sequence_length,
        ).images[0]

        buffered = BytesIO()
        images.save(buffered, format="PNG")
        img_bytes = buffered.getvalue()
        images_64 = base64.b64encode(img_bytes)

        return images_64

Next, run the following command to build the container image:

cog build

This step will use the configuration defined in the cog.yaml to create the container image and store it in local container registry. The step can take quite some time to complete, as it downloads all the dependencies, such as required libraries and the model weights, and builds the container image.

Push the container image to a remote container registry

When the previous step has completed, you should see the container image in your local container registry. To verify, please run:

docker image ls

You should see something similar to this, where you have the prefix cog- followed by folder name flux-schnell (this may be different, if you used a different folder name).

REPOSITORY                                         TAG       IMAGE ID       CREATED       SIZE
cog-flux-schnell                                   latest    8794f120a61b   5 minutes ago 17.1GB
...

Next, tag the image and push it to your remote container registry. We do not support pulling containers with the :latest tag in order to make sure that all deployments are consistent. Please make sure you use distinct tags for your container updates.

docker tag cog-flux-schnell:latest ghcr.io/username/cog-flux-schnell:v1
docker push ghcr.io/username/cog-flux-schnell:v1

This will push the container image to your remote registry. Uploading the image to the container registry can take some time, depending on your network connection.

Create the deployment

In this example, we will deploy the image we created earlier on NVIDIA L40S (48 GB VRAM) GPU type. For larger models, you may need to choose one of the other GPU types we offer.

Log in to the DataCrunch cloud dashboard
Create a new project or use existing one, open the project
On the left you'll see a navigation menu. Go to Containers -> New deployment. Name your deployment and select the L40S Compute Type.
Set Container Image to point to your repository where you pushed the image you created earlier. For example toghcr.io/username/cog-flux-schnell:v1
You can use the Public option for your image, if you pushed the image to a public repository. You can use the Private if you have a private registry, paired with credentials.
Make sure your preferred tag is selected
Set the Exposed HTTP port to 5000
Set the Healthcheck port to 5000
Set Health Check to /health-check
Make sure Start Command is off
Deploy container

(You can leave the Scaling options to their default values for now)

That's it! You have now created a deployment. You can check the logs of the deployment from the logs tab. This will take few minutes to complete.

For production use, we recommend authenticating/using private registries to avoid potential rate limits imposed by public container registries.

Accessing the deployment

Before you can connect to the endpoint, you will need to generate an authentication token, by going to Keys -> Inference API Keys, and click Create.

The base endpoint URL for your deployment is in the Containers API section in the top left of the screen. This will be in the form of: https://containers.datacrunch.io/<NAME-OF-OUR-DEPLOYMENT>/

Test Deployment

Once the deployment has been created and is ready to accept requests, you can test that it responds correctly by sending a /health-check request to the endpoint. Below is an example cURL command for running your test deployment:

Notice the added subpath /health-check to the base endpoint URL

#!/bin/bash
curl -X GET <YOUR_CONTAINERS_API_URL>/health-check \
--header 'Authorization: Bearer <YOUR_INFERENCE_API_KEY>' \
--header 'Content-Type: application/json'

This should return a response that shows the deployment is available for use.

{
  "status":"READY",
  "setup":{
    "started_at":"2025-01-22T16:12:48.859125+00:00",
    "completed_at":"2025-01-22T16:14:01.224369+00:00",
    "logs":"\rLoading pipeline components...:   0%|  ...",
    "status":"succeeded"
  }
}

Sending inference requests

After /health-check we are ready to send an inference requests to the model.

Generate image from text

Navigate to your project directory and create a new virtual environment and run commands below:

python -m venv venv
source ./venv/bin/activate

You may also need to install some required pacakges,

pip install requests

In the same folder, create a new file named inference.py and add the following code:

import requests
import base64
import sys
import signal
import time

def graceful_shutdown(signum, frame) -> None:
    print(f"\nSignal {signum} received at line {frame.f_lineno} in {frame.f_code.co_filename}")
    sys.exit(0)

def do_test_request() -> None:
    url = '<YOUR_CONTAINERS_API_URL>/predictions'

    headers = {
        'Content-Type': 'application/json',
        'Authorization': 'Bearer <YOUR_INFERENCE_API_KEY>',
    }

    data = {
        "input": {
            "prompt": "Create me an artistic and psychedelic picture of a man flying a hot air balloon above a city. The city is on fire and the balloon is made out of cotton candy.",
            "guidance_scale":"0.0",
            "height": "512",
            "width": "512",
            "num_inference_steps": "4",
            "max_sequence_length": "256",
        }
    }

    start_time = time.time()
    formatted_start_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(start_time))
    print(f"{formatted_start_time} Sending inference request")
    response = requests.post(url, headers=headers, json=data)
    if response.status_code == 200:
        try:
            response_json = response.json()
            base64_image = response_json.get('output')

            if base64_image:
                image_data = base64.b64decode(base64_image)

                with open(f'output.png', 'wb') as f:
                    f.write(image_data)

                end_time = time.time()
                formatted_end_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(end_time))
                print(f"{formatted_end_time} Image saved as output.png, Duration: {end_time - start_time} seconds")
            else:
                print("No image data found in the response.", file=sys.stderr)
        except ValueError:
            print("Response content is not valid JSON.", file=sys.stderr)
            print("Response body:", file=sys.stderr)
            print(response.text, file=sys.stderr)
    else:
        print(f"Request failed with status code {response.status_code}", file=sys.stderr)
        print("Response body:", file=sys.stderr)
        print(response.text, file=sys.stderr)

if __name__ == "__main__":
    signal.signal(signal.SIGINT, graceful_shutdown)
    signal.signal(signal.SIGTERM, graceful_shutdown)
    do_test_request()

Run it with the following command:

python inference.py

The image you generated is located in the folder you ran the script in, named output.png.

Conclusion

This concludes our tutorial how create images from text using Cog with black-forest-labs/FLUX.1-schnell model. You can now use the Cog endpoint to generate more images from text descriptions.

Last updated 1 day ago

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